The document outlines a biostatistics course aimed at preventive medicine, covering topics such as types of variables, statistical estimation, hypothesis testing, and data analysis using software like SPSS. It emphasizes the importance of biostatistics for physicians to interpret vital statistics, evaluate research studies, and understand epidemiologic problems. Additionally, it discusses data collection methods, types of variables, data organization, and presentation techniques in statistical analysis.

1. Introduction to
biostatistics course
and data sources
Dr Hamadi Widad 2024

2. Course description
The course covers the concepts and uses of biostatistics in
preventive medicine.
It includes the following topics:
1)Types of variables
2)Graphical and descriptive techniques to summarize data
3)Probability theory and rules
4)Normal and binomial distributions
5)Statistical estimation (point estimates and confidence
intervals)
6)Hypothesis testing (significance tests, e.g., t-tests, chi-
square tests, nonparametric tests,…)

3. Course description
7) Correlations
Measures of associations such as odds ratios,
relative risks, and risk differences
8) Basic concepts of ANOVA; and interpreting the
results of statistical analyses in order to provide
evidence.
9) Use of statistical computing software to perform
data analyses (SPSS)

4. Introduction

6. it is a field of study concerned with :
it is a field of study concerned with :
The collection, organization, summarization and
The collection, organization, summarization and
analysis of data.
analysis of data.

7. When the data being analyzed are
derived from the biological sciences
and medicine

8. Why Preventive Medicine
Why Preventive Medicine
physicians should learn
physicians should learn
biostatistics
biostatistics
?
?

9. • Interpreting vital statistics (births, deaths..)
how they are measured? What they mean?
How they are used?
• Understand and evaluate published scientific
research papers to decide whether they can
believe the results presented in the literature.

10. • Understanding epidemiologic problems 
prevalence of disease, variation by season or
area and influence of risk factors  to make
diagnoses and develop management plans.
• Interpreting information about drugs &
equipment  companies use graphs, charts &
results of studies comparing their products with
others in the market.

11. • Using diagnostic procedures  to know how
the test is sensitive to diagnose a disease
(Sensitivity) and also no disease (Specificity) in
a well person.
• Research project planning  Design 
Execution (Data collection)  Data processing
 Data analysis Presentation 
Interpretation  Publication

12. Primary
Primary
This is when data
collection is designed
specifically for the study
and the data are newly
collected
.
Secondary
Secondary
These are data collected
and recorded for another
research study, and
which are available for
use
.
Sources of data

13. Records Experiment
Survey

14. A- From records (Regular/Routine collection
system):
 Hospital records.
Annual vital records  births, deaths.
Annual & monthly reports of WHO,
Ministry of Health.
Journals.
Electronic data bases

15. B- Survey methods
1)Comprehensive survey (census) collection of
data about every individual in the society
 It takes great deal of money, effort and time
 Census is carried out every 10 years

16. (2) Sample survey
It is collection of data about a portion of
the population named the sample.
It requires less time, money and effort
than the comprehensive survey.
 A sample should be representative

17. C-Experiments  carried out in the
laboratory or field

18. Constant
Constant
Observation which do
not vary from time to time
or from person to person
 number of fingers,
number of eyes
Variable
Variable
A characteristic that takes
on different values in
different persons, places or
things  sex, age

19. The type of variable can be critically
The type of variable can be critically
important in determining method of
important in determining method of
analysis that will be used.
analysis that will be used.

20. Categorical
Categorical
(Qualitative)
(Qualitative)
Variables which take
the form of qualities
or names
Numerical
Numerical
(Quantitative)
(Quantitative)
Variables expressed
in the form of
quantities

21. Categorical variables:
Nominal
Satisfaction status: (satisfied, neutral, not satisfied)

22. Categorical variables:
Nominal
Sex: (female, male)

23. Categorical variables:
Nominal
Colors: (red, green, blue, pink)

24. Categorical variables:
Nominal
Nationality: (all countries)

25. Categorical variables:
Ordinal
BMI status: (underweight, normal, overweight,
obese, extremely obese)

26. Categorical variables:
Ordinal
Agreement level: (strongly disagree, disagree,
undecided, agree, strongly agree)

27. Categorical (Qualitative)
A- Two categories (Binary = Dichotomous=0/1):
These often relate to the presence or absence of some attribute
Male/female
Disease/No disease
Married/Unmarried
Diabetic/non diabetic
Smoker/non smoker
Hypertensive/normotensive
B- More than two categories:
1= Nominal
Country of birth
Blood group A/B/AB/O
Married/Single/Divorced/Separated/Widow
2= Ordinal: qualitative variables whose categories can be put in a definite order
Non-smoker/ex-smoker/light smoker/heavy smoker
Degree of pain: minimal-moderate-severe-unbearable
Stages of breast cancer: I, II, III, IV.

28. Numerical (Quantitative)
A- Continuous:
Obtained by measurement
Can take integer or fractional values
Can take any value between two fixed limits (upper/lower)
Age/Weight/Height/Distance/Blood pressure/temperature
B- Discrete:
Obtained by enumeration or counting
Number of children
Number of beds
Pulse rate
Number of visits to the GP in a year

30. Any quantitative variable can be
transformed to qualitative variable

31. What are the types of data variables in this
dataset?

32. DATA ORGANIZATION

33. By definition, a statistical study collects data on a range
of individuals . Each of these subjects is called a
statistical unit. Each observation or measurement made
on each statistical unit is one of the values of the
variable studied. The first step in describing the data is
to sort the data, group it and possibly transform it in
order to visualize it in a condensed form that allows us
to understand its distribution globally.

34. This operation consists of organizing in a coherent way the
masses of data of a quantitative or qualitative ordinal
variable. Sorting consists of arranging statistical units in
ascending or descending order of values. When studying a
qualitative variable, the statistical units are grouped
according to the different classes of that variable.
A- Sorting data

35. When studying a quantitative variable on a large number of
individuals, it is necessary to group the data to present them
clearly.
-This operation results in transforming a continuous
quantitative variable into a discrete quantitative variable.
This process is called Discretization
B- Group into classes

36. Exemple : Discretization of continuous variable

37. We can choose:
scale per amplitude, by dividing the values of the series into equal
intervals. the number of subjects per class is irregular .
The variable obtained is of the discrete quantitative type.
scale per frequency, by dividing the observed series into groups of
equal numbers, the intervals are irregular
the variable obtained is of the discrete quantitative type.
Ordinal scale of ; chosen by the operator according to the relevance of
the terminals. This choice depends on what we want to show.
The variable obtained is of the qualitative ordinal type

39. The class amplitude is the difference between the lower and upper
limits of the class
There is no rule to impose the number of classes, but if we choose an
equal amplitude for the classes, the following rule could be proposed:
Number of classes = range/amplitude.
-The range (margin) is the difference between the largest and lowest
value in the series.
-For example, if we divide an age series of 10 years into 10 years, it
should be specified that a subject who is 10 years old or older is classified
in the 10-19 age group. A subject of 9 years and 11 months will be
classified in the 0-9 age group.

40. Tabular
Tabular Graphical
Graphical Mathematical
Mathematical

41. A- Tables:
1- Raw table of data:
This is the basic work table.
-All data is included, unit by unit and variable by variable.
-Individuals are in a row, variables in columns.
- Such a table generally includes two kinds of variables: the
variables used to identify each statistical unit and the variables
measured for the study

42.  This type of table is rarely presentable as is, unless the
data is scarce as this table below:

43. They are used to present a set of data in aggregate form.
-Table: is a matrix with at least two entries, one horizontal (rows) and one
vertical (columns).
- One of the inputs is the classes of the variable.
-The second is made up of the numbers of subjects in each class of the variable
studied or their frequencys.
- A correct table must present the total number of effectives in the series
studied and the total frequencys ( percentage) to show that the classes are
exclusive.
2- Frequency table

44. 44
Elements of a Table
Ideal table should have
Number
Title
Column headings
Foot-notes
Number - Table number for identification in a report
Title, place - On top of table
(What, where and when)
Column - Variable name, No. , Percentages (%), etc.,
Heading
Foot-note(s) - to describe some column/row
headings, special cells, source, etc.,

45. Distribution of a sample of adults by blood
group in Taif 2020
Percent for group A = (5/20)*100

46. 3-Contingency table
value of the row variable; R is the number of rows.
 One column for each value of the column variable; C is the
number of columns.
One row for each value of the row variable; R is the number of
rows.
R x C contingency table.
each value of the column variable; C is the number of columns.
One row for each value of the row variable; R is the number of rows.
R x C contingency table.

47. Length of stay by gender for a sample
of inpatients in king Faisal hospital 2020
Contingency tables

50. Thank you
Thank you